We have assembled a large team to develop a Digital Twin (DT) of metals additive manufacturing (MAM), starting with the feedstock and predicting location-specific fatigue performance in parts. The effort is supported by NASA as a Science & Technology Research Institute and is entitled “Institute for Model-based Q&C of Additive Manufacturing” (IMQCAM). All component models of the DT will incorporate uncertainty quantification (UQ) which is propagated through the workflow. The project focuses on two exemplary materials commonly used in MAM, i.e., Ti-6Al-4V (Ti64) and nickel alloy 718. Prior work on processing-structure-properties [1] established a close connection between the defect structure and fatigue performance albeit for non-heat treated material from laser powder bed fusion (LPBF). In terms of micromechanical simulation, Pinz et al. [2] demonstrated an advanced crystal plasticity model in a finite element framework for modeling fatigue in MAM materials. The overall concept is that of a model-based material definition (MBMD) for the DT that incorporates integrated, interdisciplinary modeling workflows which provide a holistic engineering framework for additive manufactured materials and processes [3]. The presentation will lay out the organization of the team and the major thrusts embodied in the IMQCAM and will address the current challenges around data curation & exchange, communication between models, UQ, validation, and probabilistic approaches to prediction of fatigue. In summary, the goal of the IMQCAM is to develop an end-to-end model or digital twin of the LPBF process, connecting feedstock, 3D printing conditions, and post-print microstructural state to part response and location-specific properties, as well as fatigue life that is crucial to the aerospace community.